from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass





@dataclass
class     lowerCAmelCase__     :


		'''simple docstring'''






		lowerCAmelCase_     =   42
		lowerCAmelCase_     =   42





class     lowerCAmelCase__     :


		'''simple docstring'''






		def __init__(       self     :     int     ,  snake_case__     :     int		)      ->	Any:
				_lowerCamelCase				   =  [[] for _ in range(snake_case__		)]
				_lowerCamelCase				   =  size
		def __getitem__(       self     :     Dict     ,  snake_case__     :     int		)      ->	Iterator[Edge]:
				return iter(self._graph[vertex]		)
		@property
		def   _snake_case       (       self     :     str		)      ->	int:
				return self._size
		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     int     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	Tuple:
				if weight not in (0, 1):
						raise ValueError('Edge weight must be either 0 or 1.'		)

				if to_vertex < 0 or to_vertex >= self.size:
						raise ValueError('Vertex indexes must be in [0; size).'		)

				self._graph[from_vertex].append(Edge(snake_case__     ,  snake_case__		)		)



		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	int | None:
				_lowerCamelCase				   =  deque([start_vertex]		)
				_lowerCamelCase				   =  [None] * self.size
				_lowerCamelCase				   =  0

				while queue:
						_lowerCamelCase				   =  queue.popleft()
						_lowerCamelCase				   =  distances[current_vertex]
						if current_distance is None:
								continue

						for edge in self[current_vertex]:
								_lowerCamelCase				   =  current_distance + edge.weight
								_lowerCamelCase				   =  distances[edge.destination_vertex]
								if (
								    isinstance(snake_case__     ,  snake_case__		)
								    and new_distance >= dest_vertex_distance
								):
										continue
								_lowerCamelCase				   =  new_distance
								if edge.weight == 0:
										queue.appendleft(edge.destination_vertex		)
								else:
										queue.append(edge.destination_vertex		)

				if distances[finish_vertex] is None:
						raise ValueError('No path from start_vertex to finish_vertex.'		)

				return distances[finish_vertex]


if __name__ == "__main__":
				import doctest

				doctest.testmod()
import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):


		'''simple docstring'''






		lowerCAmelCase_     =   ShapEImgaImgPipeline
		lowerCAmelCase_     =   ['image']
		lowerCAmelCase_     =   ['image']
		lowerCAmelCase_     =   [
		    'num_images_per_prompt',
		    'num_inference_steps',
		    'generator',
		    'latents',
		    'guidance_scale',
		    'frame_size',
		    'output_type',
		    'return_dict',
		]
		lowerCAmelCase_     =   False
		@property
		def   _snake_case       (       self     :     List[str]		)      ->	Any:
				return 3_2
		@property
		def   _snake_case       (       self     :     int		)      ->	Dict:
				return 3_2
		@property
		def   _snake_case       (       self     :     Tuple		)      ->	Optional[Any]:
				return self.time_input_dim * 4
		@property
		def   _snake_case       (       self     :     Tuple		)      ->	Optional[Any]:
				return 8
		@property
		def   _snake_case       (       self     :     int		)      ->	Dict:
				torch.manual_seed(0		)
				_lowerCamelCase				   =  CLIPVisionConfig(
				    hidden_size=self.text_embedder_hidden_size     ,  image_size=6_4     ,  projection_dim=self.text_embedder_hidden_size     ,  intermediate_size=3_7     ,  num_attention_heads=4     ,  num_channels=3     ,  num_hidden_layers=5     ,  patch_size=1     ,  )

				_lowerCamelCase				   =  CLIPVisionModel(snake_case__		)
				return model
		@property
		def   _snake_case       (       self     :     Tuple		)      ->	Any:
				_lowerCamelCase				   =  CLIPImageProcessor(
				    crop_size=2_2_4     ,  do_center_crop=snake_case__     ,  do_normalize=snake_case__     ,  do_resize=snake_case__     ,  image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]     ,  image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]     ,  resample=3     ,  size=2_2_4     ,  )

				return image_processor
		@property
		def   _snake_case       (       self     :     List[str]		)      ->	Any:
				torch.manual_seed(0		)

				_lowerCamelCase				   =  {
				    'num_attention_heads': 2,
				    'attention_head_dim': 1_6,
				    'embedding_dim': self.time_input_dim,
				    'num_embeddings': 3_2,
				    'embedding_proj_dim': self.text_embedder_hidden_size,
				    'time_embed_dim': self.time_embed_dim,
				    'num_layers': 1,
				    'clip_embed_dim': self.time_input_dim * 2,
				    'additional_embeddings': 0,
				    'time_embed_act_fn': 'gelu',
				    'norm_in_type': 'layer',
				    'embedding_proj_norm_type': 'layer',
				    'encoder_hid_proj_type': None,
				    'added_emb_type': None,
				}

				_lowerCamelCase				   =  PriorTransformer(**snake_case__		)
				return model
		@property
		def   _snake_case       (       self     :     List[Any]		)      ->	Optional[int]:
				torch.manual_seed(0		)

				_lowerCamelCase				   =  {
				    'param_shapes': (
				        (self.renderer_dim, 9_3),
				        (self.renderer_dim, 8),
				        (self.renderer_dim, 8),
				        (self.renderer_dim, 8),
				    ),
				    'd_latent': self.time_input_dim,
				    'd_hidden': self.renderer_dim,
				    'n_output': 1_2,
				    'background': (
				        0.1,
				        0.1,
				        0.1,
				    ),
				}
				_lowerCamelCase				   =  ShapERenderer(**snake_case__		)
				return model
		def   _snake_case       (       self     :     Any		)      ->	str:
				_lowerCamelCase				   =  self.dummy_prior
				_lowerCamelCase				   =  self.dummy_image_encoder
				_lowerCamelCase				   =  self.dummy_image_processor
				_lowerCamelCase				   =  self.dummy_renderer

				_lowerCamelCase				   =  HeunDiscreteScheduler(
				    beta_schedule='exp'     ,  num_train_timesteps=1_0_2_4     ,  prediction_type='sample'     ,  use_karras_sigmas=snake_case__     ,  clip_sample=snake_case__     ,  clip_sample_range=1.0     ,  )
				_lowerCamelCase				   =  {
				    'prior': prior,
				    'image_encoder': image_encoder,
				    'image_processor': image_processor,
				    'renderer': renderer,
				    'scheduler': scheduler,
				}

				return components
		def   _snake_case       (       self     :     List[str]     ,  snake_case__     :     str     ,  snake_case__     :     Optional[Any]=0		)      ->	Union[str, Any]:
				_lowerCamelCase				   =  floats_tensor((1, 3, 6_4, 6_4)     ,  rng=random.Random(snake_case__		)		).to(snake_case__		)

				if str(snake_case__		).startswith('mps'		):
						_lowerCamelCase				   =  torch.manual_seed(snake_case__		)
				else:
						_lowerCamelCase				   =  torch.Generator(device=snake_case__		).manual_seed(snake_case__		)
				_lowerCamelCase				   =  {
				    'image': input_image,
				    'generator': generator,
				    'num_inference_steps': 1,
				    'frame_size': 3_2,
				    'output_type': 'np',
				}
				return inputs
		def   _snake_case       (       self     :     Union[str, Any]		)      ->	Union[str, Any]:
				_lowerCamelCase				   =  'cpu'

				_lowerCamelCase				   =  self.get_dummy_components()

				_lowerCamelCase				   =  self.pipeline_class(**snake_case__		)
				_lowerCamelCase				   =  pipe.to(snake_case__		)

				pipe.set_progress_bar_config(disable=snake_case__		)

				_lowerCamelCase				   =  pipe(**self.get_dummy_inputs(snake_case__		)		)
				_lowerCamelCase				   =  output.images[0]
				_lowerCamelCase				   =  image[0, -3:, -3:, -1]

				assert image.shape == (2_0, 3_2, 3_2, 3)

				_lowerCamelCase				   =  np.array(
				    [
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				    ]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
		def   _snake_case       (       self     :     Dict		)      ->	Optional[Any]:
				# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
				self._test_inference_batch_consistent(batch_sizes=[1, 2]		)
		def   _snake_case       (       self     :     Optional[Any]		)      ->	Tuple:
				_lowerCamelCase				   =  torch_device == 'cpu'
				_lowerCamelCase				   =  True
				self._test_inference_batch_single_identical(
				    batch_size=2     ,  test_max_difference=snake_case__     ,  relax_max_difference=snake_case__     ,  )



		def   _snake_case       (       self     :     Any		)      ->	Tuple:
				_lowerCamelCase				   =  self.get_dummy_components()
				_lowerCamelCase				   =  self.pipeline_class(**snake_case__		)
				_lowerCamelCase				   =  pipe.to(snake_case__		)
				pipe.set_progress_bar_config(disable=snake_case__		)

				_lowerCamelCase				   =  1
				_lowerCamelCase				   =  2

				_lowerCamelCase				   =  self.get_dummy_inputs(snake_case__		)

				for key in inputs.keys():
						if key in self.batch_params:
								_lowerCamelCase				   =  batch_size * [inputs[key]]

				_lowerCamelCase				   =  pipe(**snake_case__     ,  num_images_per_prompt=snake_case__		)[0]

				assert images.shape[0] == batch_size * num_images_per_prompt





@slow
@require_torch_gpu
class     lowerCAmelCase__     (				unittest.TestCase			):


		'''simple docstring'''






		def   _snake_case       (       self     :     Union[str, Any]		)      ->	Optional[int]:
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def   _snake_case       (       self     :     List[str]		)      ->	List[Any]:
				_lowerCamelCase				   =  load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png'		)
				_lowerCamelCase				   =  load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/shap_e/test_shap_e_img2img_out.npy'		)
				_lowerCamelCase				   =  ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img'		)
				_lowerCamelCase				   =  pipe.to(snake_case__		)
				pipe.set_progress_bar_config(disable=snake_case__		)

				_lowerCamelCase				   =  torch.Generator(device=snake_case__		).manual_seed(0		)

				_lowerCamelCase				   =  pipe(
				    snake_case__     ,  generator=snake_case__     ,  guidance_scale=3.0     ,  num_inference_steps=6_4     ,  frame_size=6_4     ,  output_type='np'     ,  ).images[0]

				assert images.shape == (2_0, 6_4, 6_4, 3)

				assert_mean_pixel_difference(snake_case__     ,  snake_case__		)